An Artificial Neural Network Stratifies the Risks of Reintervention and Mortality after Endovascular Aneurysm Repair; a Retrospective Observational study

Karthikesalingam, Alan; Attallah, Omneya; Ma, Xianghong; Bahia, Sandeep S.; Thompson, Luke; Vidal‐Diez, Alberto; Choke, Edward; Bown, Matthew J.; Sayers, Robert D.; Thompson, Matt; Holt, Peter

doi:10.1371/journal.pone.0129024

Cited by 55 publications

(35 citation statements)

References 46 publications

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“…Different studies indicated that ANN models can simultaneously process numerous variables and can consider outliers and nonlinear interactions among variables. Therefore, whereas conventional statistics reveal parameters that are significant only for the overall population, the ANN model includes parameters that are significant at the individual level even if they are not significant in the overall population [13–16]. …”

Section: Discussionmentioning

confidence: 99%

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Predictors of in-hospital mortality following major lower extremity amputations in type 2 diabetic patients using artificial neural networks

López‐de‐Andrés

Hernández‐Barrera

López³

et al. 2016

BMC Med Res Methodol

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BackgroundOutcome prediction is important in the clinical decision-making process. Artificial neural networks (ANN) have been used to predict the risk of post-operative events, including survival, and are increasingly being used in complex medical decision making. We aimed to use ANN analysis to estimate predictive factors of in-hospital mortality (IHM) in patients with type 2 diabetes (T2DM) after major lower extremity amputation (LEA) in Spain.MethodsWe design a retrospective, observational study using ANN models. We used the Spanish National Hospital Discharge Database to select all hospital admissions of major LEA procedure in T2DM patients. Main outcome measures: Predictors of IHM using 4 ANN models: i) with all discharge diagnosis included in the database; ii) with all discharge diagnosis included in the database, excluding infectious diseases; iii) comorbidities included in the Charlson Comorbidities Index; iv) comorbidities included in the Elixhauser Comorbidity Index.ResultsFrom 2003 to 2013, 40,857 major LEAs in patients with T2DM were identified with a 10.0% IHM. We found that Elixhauser Comorbidity Index model performed better in terms of sensitivity, specificity and precision than Charlson Comorbidity Index model (0.7634 vs 0.7444; 0.9602 vs 0.9121; 0.9511 vs 0.888, respectively). The area under the ROC curve for Elixhauser comorbidity model was 91.7% (95% CI 90.3–93.0) and for Charlson comorbidity model was 88.9% (95% CI; 87.590.2) p = 0.043. Models including all discharge diagnosis with and without infectious diseases showed worse results. In the Elixhauser Comorbidity Index model the most sensitive parameter was age (variable sensitive ratio [VSR] 1.451) followed by female sex (VSR 1.433), congestive heart failure (VSR 1.341), renal failure (VSR 1.274) and chronic pulmonary disease (VSR 1.266).ConclusionsElixhauser Comorbidity Index is a superior comorbidity risk-adjustment model for major LEA survival prediction in patients with T2DM than Charlson Comorbidity Index model using ANN models. Female sex, congestive heart failure, and renal failure are strong predictors of mortality in these patients.

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Section: Discussionmentioning

confidence: 99%

“…Artificial neural networks (ANN) have been used to predict the risk of post-operative events, including survival, exploring complex relationships between preoperative variables survival in different surgical settings and are increasingly being used in complex medical decision making [13–16]. …”

Section: Introductionmentioning

confidence: 99%

Predictors of in-hospital mortality following major lower extremity amputations in type 2 diabetic patients using artificial neural networks

López‐de‐Andrés

Hernández‐Barrera

López³

et al. 2016

BMC Med Res Methodol

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“…Further complex concerns such as endograft configuration and deployment, or intermediate markers of patients’ cardiovascular risk phenotype, could possibly be used to train ANN in prospective studies, which increases the clinical significance of prediction and makes it more reliable. Also, adding more operative factors such as graft size, and endoleak at completion or post-operative variables, such as endoleak at early surveillance scans, could significantly enhance the discriminatory power of ANNs [ 61 ]. As an evidence, the proposed FS approach using ANN has selected maximum aneurysm neck diameter, diameter of the left common iliac artery 1 and 5 mm below internal iliac ostium, maximum common iliac artery diameter 5 mm proximal to internal iliac origin, maximum iliac tortuosity index, maximum common iliac thrombus volume, and right common iliac artery non luminal volume.…”

Section: Discussionmentioning

confidence: 99%

“…In the researcher’s previously published paper [ 60 ], an uncensoring approach was proposed to deal with the high level of censoring in the EVAR datasets using MLT without performing FS. Moreover, in the researcher’s other publication [ 61 ], the proposed uncensoring approach was combined with an existing ranking FS method to reduce the number of features in the datasets. Factor analysis is a feature transformation method used to transform data into a new domain so that most of the classification related information is compressed in a smaller number of features [ 62 ].…”

Section: Introductionmentioning

confidence: 99%

Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention

Attallah

Karthikesalingam²,

Holt³

et al. 2017

BMC Med Inform Decis Mak

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BackgroundFeature selection (FS) process is essential in the medical area as it reduces the effort and time needed for physicians to measure unnecessary features. Choosing useful variables is a difficult task with the presence of censoring which is the unique characteristic in survival analysis. Most survival FS methods depend on Cox’s proportional hazard model; however, machine learning techniques (MLT) are preferred but not commonly used due to censoring. Techniques that have been proposed to adopt MLT to perform FS with survival data cannot be used with the high level of censoring. The researcher’s previous publications proposed a technique to deal with the high level of censoring. It also used existing FS techniques to reduce dataset dimension. However, in this paper a new FS technique was proposed and combined with feature transformation and the proposed uncensoring approaches to select a reduced set of features and produce a stable predictive model.MethodsIn this paper, a FS technique based on artificial neural network (ANN) MLT is proposed to deal with highly censored Endovascular Aortic Repair (EVAR). Survival data EVAR datasets were collected during 2004 to 2010 from two vascular centers in order to produce a final stable model. They contain almost 91% of censored patients. The proposed approach used a wrapper FS method with ANN to select a reduced subset of features that predict the risk of EVAR re-intervention after 5 years to patients from two different centers located in the United Kingdom, to allow it to be potentially applied to cross-centers predictions. The proposed model is compared with the two popular FS techniques; Akaike and Bayesian information criteria (AIC, BIC) that are used with Cox’s model.ResultsThe final model outperforms other methods in distinguishing the high and low risk groups; as they both have concordance index and estimated AUC better than the Cox’s model based on AIC, BIC, Lasso, and SCAD approaches. These models have p-values lower than 0.05, meaning that patients with different risk groups can be separated significantly and those who would need re-intervention can be correctly predicted.ConclusionThe proposed approach will save time and effort made by physicians to collect unnecessary variables. The final reduced model was able to predict the long-term risk of aortic complications after EVAR. This predictive model can help clinicians decide patients’ future observation plan.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-017-0508-3) contains supplementary material, which is available to authorized users.

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“…Parallel topology is the most common way used to connect classifiers [48], so it is adopted in this paper.…”

Section: Multiple Classifiers Systemmentioning

confidence: 99%

Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection

Attallah

Karthikesalingam

Holt

et al. 2017

Proc Inst Mech Eng H

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Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan.

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An Artificial Neural Network Stratifies the Risks of Reintervention and Mortality after Endovascular Aneurysm Repair; a Retrospective Observational study

Cited by 55 publications

References 46 publications

Predictors of in-hospital mortality following major lower extremity amputations in type 2 diabetic patients using artificial neural networks

Predictors of in-hospital mortality following major lower extremity amputations in type 2 diabetic patients using artificial neural networks

Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention

Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection

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